1. Field of the Invention
The invention generally relates to the analysis of plant stress in agricultural crops. In particular, the invention provides a method to remotely assess plant stress via aerial digital cameras filtered with two narrow (xe2x89xa625 nanometers) spectral bandpasses representing the absorption spectrum of Chlorophyll xcex1 at 680 nm (red) and biomass reflectance at 770 nm (far red).
2. Background of the Invention
The ability to forecast yields and diagnose crop conditions prior to harvest is important for several reasons. Models that can effectively predict yields increase the opportunity to mitigate factors that may be adversely affecting the final yield at harvest, for example nutrient application or soil and water variability. To that end, the acquisition of remote sensor data has been introduced into precision agriculture and its use is increasingly popular. The development of small format systems are making it possible to acquire such data quickly and relatively inexpensively, and are also providing an added dimension for analysis when combined with geographic information systems (GIS).
The vast majority of remote sensor data presently being used by growers is in the form of the standard color infrared photograph. These data can be obtained through a variety of sources, including local extension services. However, they frequently do not possess the scale and temporal coverage required to perform truly efficient farm management. With the introduction of small format digital multispectral camera systems, growers now have the ability to acquire data that is multi-temporal and high in spectral and spatial fidelity. Unfortunately, given the amount of data multispectral imagery can produce, the agricultural community may easily suffer from a surfeit of information. If too much uncorrelated information is available at any one time, it becomes difficult to make effective management decisions. The most effective way for remote sensing to be an asset to the agricultural community at large is to tailor crop and regional specific models that can be validated and integrated into existing GIS databases. Also, in order for farmers to avail themselves of the technology, it must be available at a reasonable price.
Numerous studies indicate strong relationships between reflectance, nutrient concentration (N) and productivity of crops. Walberg et al. (1982) successfully demonstrated the synergistic effects of these parameters on corn plants. Also, Schepers et al. (1996) determined specific wavelengths that were particularly effective in determining nutrient and water content for corn. Chappelle et al. (1992) discovered that specific band ratios could be used to determine chlorophyll A (CHLA), chlorophyll B (CHLB), and carotenoid concentrations in soybean plants. Other studies relating optical measurements to plant productivity involve the use of chlorophyll meters that measure the reflectance or transmittance of leaf chlorophyll sampled in vivo (Blackmer and Schepers, 1994). Peng et al. (1996) and Garcia et al. (1996) recently described the effective use of chlorophyll meters to manage high yield rice. However, while such field techniques can be effective in productivity analysis, they do not provide a synoptic way to view crops that can be integrated with other spatial data such as GIS.
There is a need in the field of agriculture for remote sensing models that provide remote sensing of crops that can be integrated with other spatial data, that are demonstrably efficient, that are intelligible to the grower, and that are available at reasonable prices.
It is an object of this invention to provide a method for assessing plant stress in agricultural crops.
It is another object of this invention to provide a method to predict crop yields from non-invasive measurements.
According to the system of the invention, a plurality of digital cameras are employed for taking images of a stand of vegetation at different wavelengths. One of the cameras takes images at wavelengths associated with the absorption spectrum of chlorophyll, and another takes images at wavelengths associated with biomass reflectance. The system also includes a means to correlate the images taken by the cameras and to compute an Algorithm Image for the stand of vegatation, as well as a means to display the calculated Algorithm Image.
The invention provides a method for predicting plant stress. This is accomplished by gathering a plurality of digital images using a photographic system comprised of multiple digital cameras equipped with narrow bandpass filters set to detect wavelengths of light corresponding to specific pigment and biomass response regions for vegetation, correcting the digital data from the images, and transforming the data to an Algorithm Image which represents the chlorophyll content for the stand of vegetation. The Algorithm Image is then interpreted to predict crop yields.
In a preferred embodiment of the invention, the digital cameras are connected to a fixed wing aircraft, and the images are gathered via aerial photography. In a preferred embodiment of the instant invention, the images are gathered while maintaining a high sun angle and bi-directional effects are limited by using, for example, a north to south flight line.
The transformation of the digital data into an Algorithm Image is accomplished by calculating a ratio which results in a Red/Far Red Data Product, and combining the digital image of the chlorophyll absorption wavelength with the Red/Far Red Data Product. The Algorithm Image is created by mapping the first digital image and the Red/Far Red Data Product to different color guns.
The Algorithm Image is interpreted by comparing the variations in intensity of the colors which are selected for the mapping step. In general, the brightest, most intense colors indicate higher chlorophyll content, and in general, high chlorophyll content will correlate positively with high crop yields.